On-vehicle camera alignment monitoring system

ABSTRACT

A system for on-vehicle camera alignment monitoring includes an on-vehicle camera in communication with a controller. The controller monitors vehicle operating parameters and camera signal parameters, and captures an image file from the on-vehicle camera. A first level analysis of the image file, the vehicle operating parameters, and the camera signal parameters is executed to detect dynamic conditions and image feature parameters that affect camera alignment. An error with one of the dynamic conditions or the image feature parameters that affects the camera alignment is detected. A second level analysis of the camera signal parameters is executed to identify a root cause indicating one of the dynamic conditions or the image feature parameters that affects the camera alignment based upon the error. A camera alignment-related fault is detected based upon the root cause, and vehicle operation is controlled based upon the camera alignment-related fault.

Vehicles may include on-board cameras for monitoring an environmentproximal to a vehicle during operation, to operate advanced driverassistance systems (ADAS) and/or autonomically-operated vehiclefunctions. Correct alignment of one or more on-vehicle cameras relativeto a reference such as ground is necessary for operation of a bird's eyeview imaging system, travel lane sensing, autonomic vehicle control,etc. A set of parameters with six degrees-of-freedom (x, y, z, roll,pitch and yaw) is used to represent the transform from a cameracoordinate system to a reference coordinate system. An alignment processruns offline and/or online to determine these parameters. Analignment-related fault of an on-vehicle camera refers to a fault in thealignment process, which may be caused by system hardware issues, dataquality issues, system degradation, vibration, an undetected or unwantedmechanical adjustment, etc. Presence of camera alignment-related faultmay degrade performance of a spatial monitoring system and an autonomicvehicle control system due to its effect upon camera to groundalignment.

As such, there is a need for a method, system and apparatus to monitorand detect a misalignment of an on-vehicle camera, identify a root causetherefor, and dynamically adjust or otherwise compensate cameraalignment in response.

SUMMARY

The concepts described herein provide a vehicle that includes amonitoring system to dynamically detect misalignment of an on-vehiclecamera, identify a root cause therefor, and dynamically adjust orotherwise compensate camera alignment in response, including controllingoperation of the vehicle based thereon.

In one embodiment, a system for on-vehicle camera alignment monitoringfor a vehicle spatial monitoring system is described that includes anon-vehicle camera in communication with a controller. The controllerincludes an instruction set that is executable to monitor vehicleoperating parameters and camera signal parameters, and capture an imagefile from the on-vehicle camera. A first level analysis of the imagefile, the vehicle operating parameters, and the camera signal parametersis executed to detect dynamic conditions and a plurality of imagefeature parameters that affect camera alignment. An error with one ofthe dynamic conditions or the plurality of image feature parameters thataffects the camera alignment is detected. A second level analysis of thecamera signal parameters is executed to identify a root cause indicatingone of the dynamic conditions or the image feature parameters thataffects the camera alignment based upon the error. A cameraalignment-related fault is detected based upon the root cause, andvehicle operation is controlled, adapted, disabled or otherwisemitigated based upon the camera alignment-related fault.

An aspect of the disclosure includes the dynamic conditions includingvehicle speed, acceleration and yaw rate, and wherein the instructionset is executable to detect the error when one of the vehicle speed, theacceleration, or the yaw rate is outside a respective allowable rangebased upon the dynamic conditions.

Another aspect of the disclosure includes the dynamic conditions being aroad surface, and wherein the instruction set is executable to detectthe error when an uneven road surface is detected.

Another aspect of the disclosure includes the plurality of image featureparameters being at least one of a feature extraction count, a featurematch count, an essential matrix inlier point count, a recovering posefeature count, a triangulation inlier point count, a two-dimensionalroad region of interest (ROI) feature point count, a three-dimensionalroad ROI feature point count, and a plane-fitting inlier point count.

Another aspect of the disclosure includes the instruction set beingexecutable to detect the error when the one of the plurality of imagefeature parameters is outside a respective allowable range.

Another aspect of the disclosure includes the instruction set beingexecutable to identify one of an insufficient lighting condition, a lensblockage, or an inclement weather condition when one of the plurality ofimage feature parameters is outside a respective allowable range.

Another aspect of the disclosure includes the instruction set beingexecutable to capture a plurality of consecutive image files from theon-vehicle camera, determine a plurality of matched feature pairsbetween the plurality of consecutive image files, determine a pluralityof motion vectors based upon the plurality of matched feature pairs, anddetect an error with the plurality of motion vectors that affects thecamera alignment. The second level analysis of the camera signalparameters is executed to identify the root cause of the error with theplurality of motion vectors that affects the camera alignment; whereinthe root cause of the error with the plurality of motion vectors thataffects the camera alignment includes a fault with mounting of theon-vehicle camera.

Another aspect of the disclosure includes the instruction set beingexecutable to detect a camera alignment-related fault based upon theroot cause of the error with the plurality of motion vectors thataffects the camera alignment, and control, adapt, disable or otherwisemitigate vehicle operation based upon the camera alignment-relatedfault.

Another aspect of the disclosure includes the instruction set beingexecutable to cluster the plurality of motion vectors to determineinlier points in an essential matrix calculation, a triangulation andplane fitting; and identify an insufficient quantity of inlier pointsbased thereon.

Another aspect of the disclosure includes the instruction set beingexecutable to control, adapt, disable or otherwise mitigate vehicleoperation based upon the camera alignment-related fault by notifying avehicle operator of the camera alignment-related fault.

Another aspect of the disclosure includes an autonomic vehicle controlsystem operatively connected to the vehicle spatial monitoring system;wherein the instruction set is executable to disable the autonomicvehicle control system based upon the camera alignment-related fault.

Another aspect of the disclosure includes communicating the image file,the vehicle operating parameters, the camera signal parameters, and theplurality of image feature parameters to an off-board system.

Another aspect of the disclosure includes a system for on-vehicle cameraalignment monitoring that includes a vehicle spatial monitoring systemhaving an on-vehicle camera in communication with a controller. Thecontroller includes an instruction set that is executable to monitorvehicle operating parameters and camera signal parameters, capture aplurality of image files from the on-vehicle camera, and analyze theplurality of image files, the vehicle operating parameters, and thecamera signal parameters to detect dynamic conditions, a plurality ofimage feature parameters, and a plurality of motion vectors that affectcamera alignment. An error with one of the dynamic conditions, theplurality of image feature parameters, and the plurality of motionvectors that affects the camera alignment is detected. The camera signalparameters are analyzed to identify a root cause indicating one of thedynamic conditions, the image feature parameters, or the plurality ofmotion vectors that affects the camera alignment based upon the error. Acamera alignment-related fault is detected based upon the root cause.Vehicle operation is controlled, adapted, disabled or otherwisemitigated based upon the camera alignment-related fault.

The above features and advantages, and other features and advantages, ofthe present teachings are readily apparent from the following detaileddescription of some of the best modes and other embodiments for carryingout the present teachings, as defined in the appended claims, when takenin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 schematically shows a vehicle including a spatial monitoringsystem and an autonomic vehicle control system, in accordance with thedisclosure.

FIG. 2 schematically illustrates an architecture for a system foron-vehicle camera alignment monitoring, in accordance with thedisclosure.

FIG. 3 schematically illustrates a first level analysis routine foron-vehicle camera alignment monitoring, in accordance with thedisclosure.

FIGS. 4-1 through 4-5 schematically illustrate a plurality ofsubroutines of the second level analysis routine for on-vehicle cameraalignment monitoring, in accordance with the disclosure.

FIG. 5-1 pictorially illustrates a raw camera image of a ROI captured indaylight, in accordance with the disclosure.

FIG. 5-2 schematically illustrates a bar graph of pixel intensitydistribution for the raw camera image of FIG. 5-1 , in accordance withthe disclosure.

FIG. 5-3 pictorially illustrates a raw camera image of the same ROI asshown in FIG. 5-1 captured at night, in accordance with the disclosure.

FIG. 5-4 schematically illustrates a bar graph of pixel intensitydistribution for the raw camera image of FIG. 5-3 , in accordance withthe disclosure.

FIG. 6 pictorially illustrates a raw camera image of a region ofinterest (ROI) in which a portion of the ROI is overshadowed by ablockage, in accordance with the disclosure.

FIG. 7-1 pictorially illustrates a raw image of a ROI of the camera witha multiplicity of feature pairs indicated, in accordance with thedisclosure.

FIG. 7-2 schematically illustrates, in 3D space, clusters of themultiplicity of feature pairs shown with reference to FIG. 7-1 , inaccordance with the disclosure.

FIG. 8-1 pictorially illustrates a raw image of a ROI of the cameraincluding a dynamic object, and a multiplicity of feature pairsindicated, in accordance with the disclosure.

FIG. 8-2 schematically illustrates, in 3D space, clusters of themultiplicity of feature pairs shown with reference to FIG. 8-1 , inaccordance with the disclosure.

It should be understood that the appended drawings are not necessarilyto scale, and present a somewhat simplified representation of variouspreferred features of the present disclosure as disclosed herein,including, for example, specific dimensions, orientations, locations,and shapes. Details associated with such features will be determined inpart by the particular intended application and use environment.

DETAILED DESCRIPTION

The components of the disclosed embodiments, as described andillustrated herein, may be arranged and designed in a variety ofdifferent configurations. Thus, the following detailed description isnot intended to limit the scope of the disclosure, as claimed, but ismerely representative of possible embodiments thereof. In addition,while numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theembodiments disclosed herein, some embodiments can be practiced withoutsome of these details. Moreover, for the purpose of clarity, certaintechnical material that is understood in the related art has not beendescribed in detail in order to avoid unnecessarily obscuring thedisclosure. Furthermore, the disclosure, as illustrated and describedherein, may be practiced in the absence of an element that is notspecifically disclosed herein.

Referring to the drawings, wherein like reference numerals correspond tolike or similar components throughout the several Figures, FIG. 1 ,consistent with embodiments disclosed herein, illustrates a top view ofa vehicle 10 disposed on a ground surface 50 and having a vehiclespatial monitoring system 40 that is illustrative of the conceptsdescribed herein. In one embodiment, the vehicle 10 also includes anautonomic vehicle control system 20. The vehicle 10 may include, in oneembodiment, a four-wheel passenger vehicle with steerable front wheelsand fixed rear wheels. The vehicle 10 may include, by way ofnon-limiting examples, a passenger vehicle, a light-duty or heavy-dutytruck, a utility vehicle, an agricultural vehicle, anindustrial/warehouse vehicle, or a recreational off-road vehicle.

The vehicle spatial monitoring system 40 and spatial monitoringcontroller 140 can include a controller that communicates with aplurality of cameras 41 to monitor fields of view proximal to thevehicle 10 and generate digital representations of the fields of viewincluding proximate remote objects.

The spatial monitoring controller 140 can evaluate inputs from thecameras 41 to determine a linear range, relative speed, and trajectoryof the vehicle 10 in relation to each proximate remote object.

The cameras 41 are located at various locations on the vehicle 10, andinclude a front camera 42 capable of viewing a forward region ofinterest (ROI) 52, a rear camera 44 capable of viewing a rearward ROI54, a left camera 46 capable of viewing a leftward ROI 56, and a rightcamera 48 capable of viewing a rightward ROI 58. The front camera 42,rear camera 44, left camera 46 and right camera 48 are capable ofcapturing and pixelating 2D images of their respective ROIs. The frontcamera 42, rear camera 44, left camera 46 and right camera 48 mayutilize fish-eye lenses to maximize the reach of their respective ROIs.

Placement of the aforementioned cameras 41 permits the spatialmonitoring controller 140 to monitor traffic flow including proximatevehicles, other objects around the vehicle 10, and the ground surface50. Data generated by the spatial monitoring controller 140 may beemployed by a lane mark detection processor (not shown) to estimate theroadway. The cameras 41 of the vehicle spatial monitoring system 40 canfurther include object-locating sensing devices including range sensors,such as FM-CW (Frequency Modulated Continuous Wave) radars, pulse andFSK (Frequency Shift Keying) radars, and Lidar (Light Detection andRanging) devices, and ultrasonic devices which rely upon effects such asDoppler-effect measurements to locate forward objects. The possibleobject-locating devices include charged-coupled devices (CCD) orcomplementary metal oxide semi-conductor (CMOS) video image sensors, andother camera/video image processors which utilize digital photographicmethods to ‘view’ forward objects including one or more proximalvehicle(s). Such sensing systems are employed for detecting and locatingobjects in automotive applications and are useable with systemsincluding, e.g., adaptive cruise control, autonomous braking, autonomoussteering and side-object detection.

The cameras 41 associated with the vehicle spatial monitoring system 40are preferably positioned within the vehicle 10 in relativelyunobstructed positions to monitor the spatial environment. As employedherein, the spatial environment includes all external elements,including fixed objects such as signs, poles, trees, houses, stores,bridges, etc., and moving or moveable objects such as pedestrians andother vehicles. Overlapping coverage areas of the cameras 41 createopportunities for sensor data fusion.

The autonomic vehicle control system 20 includes an on-vehicle controlsystem that is capable of providing a level of driving automation, e.g.,an advanced driver assistance system (ADAS). The terms driver andoperator describe the person responsible for directing operation of thevehicle, whether actively involved in controlling one or more vehiclefunctions or directing autonomous vehicle operation. Driving automationcan include a range of dynamic driving and vehicle operation. Drivingautomation can include some level of automatic control or interventionrelated to a single vehicle function, such as steering, acceleration,and/or braking, with the driver continuously having overall control ofthe vehicle. Driving automation can include some level of automaticcontrol or intervention related to simultaneous control of multiplevehicle functions, such as steering, acceleration, and/or braking, withthe driver continuously having overall control of the vehicle. Drivingautomation can include simultaneous automatic control of all vehicledriving functions, including steering, acceleration, and braking,wherein the driver cedes control of the vehicle for a period of timeduring a trip. Driving automation can include simultaneous automaticcontrol of vehicle driving functions, including steering, acceleration,and braking, wherein the driver cedes control of the vehicle for anentire trip. Driving automation includes hardware and controllersconfigured to monitor a spatial environment under various driving modesto perform various driving tasks during dynamic operation. Drivingautomation can include, by way of non-limiting examples, cruise control,adaptive cruise control, lane-change warning, intervention and control,automatic parking, acceleration, braking, and the like.

The vehicle systems, subsystems and controllers associated with theautonomic vehicle control system 20 are implemented to execute one or aplurality of operations associated with autonomous vehicle functions,including, by way of non-limiting examples, an adaptive cruise control(ACC) operation, lane guidance and lane keeping operation, lane changeoperation, steering assist operation, object avoidance operation,parking assistance operation, vehicle braking operation, vehicle speedand acceleration operation, vehicle lateral motion operation, e.g., aspart of the lane guidance, lane keeping and lane change operations, etc.The vehicle systems and associated controllers of the autonomic vehiclecontrol system 20 can include, by way of non-limiting examples, adrivetrain 32 and drivetrain controller (PCM) 132 that is operativelyconnected to one or more of a steering system 34, a braking system 36,and a chassis system 38.

Each of the vehicle systems and associated controllers may furtherinclude one or more subsystems and one or more associated controllers.The subsystems and controllers are shown as discrete elements for easeof description. The foregoing classification of the subsystems isprovided for purposes of describing one embodiment, and is illustrative.Other configurations may be considered within the scope of thisdisclosure. It should be appreciated that the functions described andperformed by the discrete elements may be executed using one or moredevices that may include algorithmic code, calibrations, hardware,application-specific integrated circuitry (ASIC), and/or off-board orcloud-based computing systems.

The vehicle 10 has a telematics device 88, which includes a wirelesstelematics communication system capable of extra-vehicle communications,including communicating with a communication network system havingwireless and wired communication capabilities. The telematics device 88is capable of extra-vehicle communications that includes short-range adhoc vehicle-to-vehicle (V2V) communication and/or vehicle-to-everything(V2x) communication, which may include communication with aninfrastructure monitor, e.g., a traffic camera and ad hoc vehiclecommunication. Alternatively, or in addition, the telematics device 88has a wireless telematics communication system capable of short-rangewireless communication to a handheld device, e.g., a cell phone, asatellite phone or another telephonic device. In one embodiment, thehandheld device executes the extra-vehicle communication, includingcommunicating with an off-board server 80 via a communication network 85including a satellite, an antenna, and/or another communication mode.Alternatively, or in addition, the telematics device 88 executes theextra-vehicle communication directly by communicating with the off-boardserver 80 via a communication network 90. In one embodiment, theoff-board server 80 is cloud-based.

A vehicle controller (PCM) 132 communicates with and is operativelyconnected to the drivetrain 32, and executes control routines to controloperation of an engine and/or other torque machines, a transmission anda driveline, none of which are shown, to transmit tractive torque to thevehicle wheels in response to driver inputs, external conditions, andvehicle operating conditions. The PCM 132 is shown as a singlecontroller, but can include a plurality of controller devices operativeto control various powertrain actuators, including the engine,transmission, torque machines, wheel motors, and other elements of thedrivetrain 32. By way of a non-limiting example, the drivetrain 32 caninclude an internal combustion engine and transmission, with anassociated engine controller and transmission controller. Furthermore,the internal combustion engine may include a plurality of discretesubsystems with individual controllers, including, e.g., an electronicthrottle device and controller, fuel injectors and controller, etc. Thedrivetrain 32 may also be composed of an electrically-poweredmotor/generator with an associated power inverter module and invertercontroller. The control routines of the PCM 132 may also include anadaptive cruise control system (ACC) that controls vehicle speed,acceleration and braking in response to driver inputs and/or autonomousvehicle control inputs.

A VCM 136 communicates with and is operatively connected to a pluralityof vehicle operating systems and executes control routines to controloperation thereof. The vehicle operating systems can include braking,stability control, and steering, which can be controlled by actuatorsassociated with the braking system 36, the chassis system 38 and thesteering system 34, respectively, which are controlled by the VCM 136.The VCM 136 is shown as a single controller, but can include a pluralityof controller devices operative to monitor systems and control variousvehicle actuators.

The steering system 34 is configured to control vehicle lateral motion.The steering system 34 can include an electrical power steering system(EPS) coupled with an active front steering system to augment orsupplant operator input through a steering wheel by controlling steeringangle of the steerable wheels of the vehicle 10 during execution of anautonomic maneuver such as a lane change maneuver. An exemplary activefront steering system permits primary steering operation by the vehicledriver including augmenting steering wheel angle control to achieve adesired steering angle and/or vehicle yaw angle. Alternatively or inaddition, the active front steering system can provide completeautonomous control of the vehicle steering function. It is appreciatedthat the systems described herein are applicable with modifications tovehicle steering control systems such as electrical power steering,four/rear wheel steering systems, and direct yaw control systems thatcontrol traction of each wheel to generate a yaw motion.

The braking system 36 is configured to control vehicle braking, andincludes wheel brake devices, e.g., disc-brake elements, calipers,master cylinders, and a braking actuator, e.g., a pedal. Wheel speedsensors monitor individual wheel speeds, and a braking controller can bemechanized to include anti-lock braking functionality.

The chassis system 38 preferably includes a plurality of on-boardsensing systems and devices for monitoring vehicle operation todetermine vehicle motion states, and, in one embodiment, a plurality ofdevices for dynamically controlling a vehicle suspension. The vehiclemotion states preferably include, e.g., vehicle speed, steering angle ofthe steerable front wheels, and yaw rate. The on-board sensing systemsand devices include inertial sensors, such as rate gyros andaccelerometers, and collectively referred to as an inertial monitoringunit (IMU) 47. The IMU 47 measures and reports specific force, angularrate, and sometimes the orientation of the vehicle, collectivelyreferred to as roll, pitch and yaw. The vehicle 10 also includes aglobal position system (GPS) sensor 49. The chassis system 38 estimatesthe vehicle motion states, such as longitudinal speed, yaw-rate andlateral speed, and estimates lateral offset and heading angle of thevehicle 10. The measured yaw rate is combined with steering anglemeasurements to estimate the vehicle state of lateral speed. Thelongitudinal speed may be determined based upon signal inputs from wheelspeed sensors arranged to monitor each of the front wheels and rearwheels. Signals associated with the vehicle motion states that can becommunicated to and monitored by other vehicle control systems forvehicle control and operation.

The term “controller” and related terms such as control module, module,control, control unit, processor and similar terms refer to one orvarious combinations of Application Specific Integrated Circuit(s)(ASIC), electronic circuit(s), central processing unit(s), e.g.,microprocessor(s) and associated non-transitory memory component(s) inthe form of memory and storage devices (read only, programmable readonly, random access, hard drive, etc.). The non-transitory memorycomponent is capable of storing machine-readable instructions in theform of one or more software or firmware programs or routines,combinational logic circuit(s), input/output circuit(s) and devices,signal conditioning and buffer circuitry and other components that canbe accessed by one or more processors to provide a describedfunctionality. Input/output circuit(s) and devices includeanalog/digital converters and related devices that monitor inputs fromsensors, with such inputs monitored at a preset sampling frequency or inresponse to a triggering event. Software, firmware, programs,instructions, control routines, code, algorithms and similar terms meancontroller-executable instruction sets including calibrations andlook-up tables. Each controller executes control routine(s) to providedesired functions. Routines may be executed at regular intervals, forexample each 100 microseconds during ongoing operation. Alternatively,routines may be executed in response to occurrence of a triggeringevent. The term ‘model’ refers to a processor-based orprocessor-executable code and associated calibration that simulates aphysical existence of a device or a physical process. The terms‘dynamic’ and ‘dynamically’ describe steps or processes that areexecuted in real-time and are characterized by monitoring or otherwisedetermining states of parameters and regularly or periodically updatingthe states of the parameters during execution of a routine or betweeniterations of execution of the routine. The terms “calibration”,“calibrate”, and related terms refer to a result or a process thatcompares an actual or standard measurement associated with a device witha perceived or observed measurement or a commanded position. Acalibration as described herein can be reduced to a storable parametrictable, a plurality of executable equations or another suitable form.Communication between controllers, and communication betweencontrollers, actuators and/or sensors may be accomplished using a directwired point-to-point link, a networked communication bus link, awireless link or another suitable communication link. Communicationincludes exchanging data signals in suitable form, including, forexample, electrical signals via a conductive medium, electromagneticsignals via air, optical signals via optical waveguides, and the like.The data signals may include discrete, analog or digitized analogsignals representing inputs from sensors, actuator commands, andcommunication between controllers. The term “signal” refers to aphysically discernible indicator that conveys information, and may be asuitable waveform (e.g., electrical, optical, magnetic, mechanical orelectromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave,square-wave, vibration, and the like, that is capable of travelingthrough a medium. A parameter is defined as a measurable quantity thatrepresents a physical property of a device or other element that isdiscernible using one or more sensors and/or a physical model. Aparameter can have a discrete value, e.g., either “1” or “0”, or can beinfinitely variable in value.

Referring now to FIG. 2 , the concepts described herein provide asystem, method, and/or apparatus 200 to monitor alignment of the camera41, e.g., one of a front camera 42, rear camera 44, left camera 46 orright camera 48 of an embodiment of the vehicle 10 and spatialmonitoring system 40 that are described with reference to FIG. 1 .

As described with reference to FIG. 2 , an architecture for the system200 for on-vehicle camera alignment monitoring includes the camera 41 incommunication with controller 45. The controller 45 has an instructionset that is executable to monitor vehicle operating parameters andcamera signal parameters and capture an image file 99 therefrom. Thearchitecture for the system 200 includes an adaptive camera alignmentroutine (Online camera alignment) 250, a first level analysis routine(Level 1 fault diagnostics) 300 for on-vehicle camera alignmentmonitoring, a second level analysis routine (Level 2 fault diagnostics)400 for on-vehicle camera alignment monitoring, and a post-processinganalysis routine (Postprocessing) 500, and a secondary analysis routine(Secondary analysis) 550 for on-vehicle camera alignment monitoring.

The adaptive camera alignment routine 250 is an internally executedcontrol routine that dynamically monitors and adjusts alignment of thecamera 41 based upon known reference points.

The first level analysis routine 300 executes an analysis of an imagefile 99 in context of vehicle operating parameters and camera signalparameters to detect dynamic conditions, image feature parameters, and amotion vector that may affect camera alignment, may indicate cameraalignment-related fault, or may preclude further analysis due to otherfactors related to the dynamic conditions, the image feature parameters,and the motion vector. The first level analysis routine 300 is describedin detail with reference to FIG. 3 . An error 399 associated with one ofthe dynamic conditions, the image feature parameters, and the motionvector that affects the camera alignment is identified.

The second level analysis routine 400 is executed to identify a rootcause 499 associated with the one of the dynamic conditions, the imagefeature parameters, and the motion vector that affects the cameraalignment based upon the error 399 and, in some cases, information fromthe secondary analysis routine 550. The second level analysis routine400 is described in detail with reference to FIGS. 4-1 through 4-5 . Acamera alignment-related fault is detected based upon the second levelanalysis. The post-processing analysis routine 500 executespost-processing steps to control, adapt, disable or otherwise mitigatevehicle operation and perform other steps based upon the cameraalignment-related fault.

Referring now to FIG. 3 , with continued reference to the vehicle 10described with reference to FIG. 1 , the first level analysis routine300 for on-vehicle camera alignment monitoring is depicted as analgorithmic flowchart and is described in detail. Execution of the firstlevel analysis routine 300 may proceed as follows during operation ofthe vehicle 10. The steps of the first level analysis routine 300 may beexecuted in a suitable order, and are not limited to the order describedwith reference to FIG. 3 . As employed herein, the term “Y” indicates ananswer in the affirmative, or “YES”, and the term “N” indicates ananswer in the negative, or “NO”.

A fault in the alignment process may be due to violation of one or moretest enable criteria. Test enable criteria need to be met before asubstantive alignment process is executed in response to occurrence ofmisalignment of a respective one of the cameras 41. The test enablecriteria include monitoring and evaluating vehicle operating parameters.The vehicle operating parameters of speed (w), acceleration (a), and yawrate (φ) are monitored (S301), and evaluated (S302). When the speed isoutside of an allowable speed range (w<θ_(w1), or w>θ_(w2)), or the yawrate is greater than an allowable yaw rate (|φ|>θ_(α)), or vehicleacceleration in any of the x, y, or z directions is greater thancorresponding allowable accelerations (|α_(x)|>θ_(ax), |α_(y)|>θ_(ay) or|α_(z)|>θ_(az)), (S302)(Y), a first fault code is set (S303) indicatingthat dynamic vehicle conditions are violated. The first fault code andassociated image file 99 are captured in a memory device (S328) and thisiteration ends (S329).

Image feature parameters include features that may be extracted,derived, or otherwise determined from each image file 99, and mayinclude one or more of a feature extraction count c_(f), a feature matchcount c_(m), an essential matrix inlier point count c_(E), a recoveringpose feature count c_(p), triangulation inlier point count c_(t), atwo-dimensional road ROI feature point count C_(r2d), athree-dimensional road ROI feature point count C_(r3d), and aplane-fitting inlier point count C_(g). These features and counts may begenerated by the alignment routine 250, and consequently reused inroutines 300, 400 and 500. The first level analysis routine 300 detectsan error with one of the plurality of image feature parameters when theone of the plurality of image feature parameters is outside a respectiveallowable range. This operation is described with reference to StepsS304 through S325.

Otherwise (S302)(N), a feature extraction count c_(f) for the image file99 is compared to a minimum permissible feature extraction count θ_(f)(S304). When the feature count is less than the minimum permissiblefeature extraction count θ_(f) (c_(f)<θ_(f)) (5304)(Y), a second faultcode is set (S305) indicating that the feature extraction count is toolow. The second fault code and associated image file 99 are captured ina memory device (S328) and this iteration ends (S329).

Otherwise (S304)(N), a feature match count c_(m) for the image file 99is compared to a minimum permissible feature match count θ_(m) (S306).When the feature match count is less than the minimum permissiblefeature match count θ_(m) (c_(m)<θ_(m)) (S306)(Y), a third fault code isset (S307) indicating that the feature match count is too low. The thirdfault code and associated image file 99 are captured in a memory device(S328) and this iteration ends (S329).

Otherwise (S306)(N), an essential matrix inlier point count c_(E) forthe image file 99 is compared to an essential matrix inlier point countθ_(E) (S308). When the essential matrix inlier point count is less thanthe essential matrix inlier point count θ_(E) (c_(E)<θ_(E)) (S308)(Y), afourth fault code is set (S309) indicating that the essential matrixinlier point count c_(E) is too low. The fourth fault code andassociated image file 99 are captured in a memory device (S328) and thisiteration ends (S329).

Otherwise (S308)(N), a recovering pose feature count c_(p) for the imagefile 99 is compared to a minimum recovering pose feature count θ_(p)(S310). When the recovering pose feature count is less than the minimumpermissible recovering pose feature count θ_(p) (c_(f)>θ_(f)) (S310)(Y),a fifth fault code is set (S311) indicating that the recovering posefeature count is too low. The fifth fault code and associated image file99 are captured in a memory device (S328) and this iteration ends(S329).

A derived rotation matrix R is converted to values for roll, pitch andyaw of the vehicle (S312), which are compared to threshold values forroll, pitch, and yaw θ_(R) (S313). When any of the roll, pitch and yawof the vehicle (S312) are greater than the respective threshold valuesfor roll, pitch, and yaw θ_(R) (S313)(Y), a sixth fault code is set(S314) indicating that the roll, pitch and/or yaw of the vehicle is toogreat. The sixth fault code and associated image file 99 are captured ina memory device (S328) and this iteration ends (S329).

An angle β is determined between a translation vector t and a referenceground normal vector (S315). The angle β is normalized (|β−90|) andcompared to a maximum threshold angle θ_(β). When the normalized angleis greater than the maximum threshold angle θ_(β) (S316)(Y), a seventhfault code is set (S317) indicating that the angle θ_(β) is too great.The seventh fault code and associated image file 99 are captured in amemory device (S328) and this iteration ends (S329).

A triangulation inlier point count c_(t) is determined (S318). Thetriangulation inlier point count c_(t) is compared to a minimumthreshold triangulation inlier point count θ_(t). When the triangulationinlier point count c_(t) is less than the minimum triangulation inlierpoint count θ_(t) (S318)(Y), an eighth fault code is set (S319)indicating that the triangulation inlier point count c_(t) is too small.The eighth fault code and associated image file 99 are captured in amemory device (S328) and this iteration ends (S329).

A two-dimensional road ROI feature point count C_(r2d) is determined(S320). The two-dimensional road ROI feature point count C_(r2d) iscompared to a minimum two-dimensional road ROI feature point countθ_(r2d). When the two-dimensional road ROI feature point count C_(r2d)is less than the minimum two-dimensional road ROI feature point countθ_(r2d) (S320)(Y), a ninth fault code is set (S321) indicating that thetwo-dimensional road ROI feature point count Cad is too small. The ninthfault code and associated image file 99 are captured in a memory device(S328) and this iteration ends (S329).

A three-dimensional road ROI feature point count C_(r3d) is determined(S322). The three-dimensional road ROI feature point count C_(r3d) iscompared to a minimum three-dimensional road ROI feature point countθ_(r3d). When the three-dimensional road ROI feature point count C_(r3a)is less than the minimum three-dimensional road ROI feature point countθ_(r2d) (S322)(Y), a tenth fault code is set (S323) indicating that thethree-dimensional road ROI feature point count C_(r3d) is too small. Thetenth fault code and associated image file 99 are captured in a memorydevice (S328) and this iteration ends (S329).

A plane-fitting inlier point count C_(g) is determined (S324). Theplane-fitting inlier point count C_(g) is compared to a minimumplane-fitting inlier point count θ_(g). When the plane-fitting inlierpoint count C_(g) is less than the minimum plane-fitting inlier pointcount θ_(g) (S324)(Y), an eleventh fault code is set (S325) indicatingthat the plane-fitting inlier point count C_(g) is too small. Theeleventh fault code and associated image file 99 are captured in amemory device (S328) and this iteration ends (S329).

An uneven road surface flag f_(u) is evaluated (S326). When the unevenroad surface flag f_(u) has been set, indicating an uneven road surface(S326)(Y), a twelfth fault code is set (S327). The twelfth fault codeand associated image file 99 are captured in a memory device (S328) andthis iteration ends (S329).

At this point, this iteration of the first level analysis routine 300ends (S329).

Referring now to FIGS. 4-1 through 4-5 , with continued reference to thevehicle 10 described with reference to FIG. 1 , the second levelanalysis routine 400 is depicted as an algorithmic flowchart, and isdescribed in detail. to identify a root cause associated with the one ofthe dynamic conditions, the image feature parameters, and the motionvector that affects the camera alignment based upon the error 399 and,in some cases, information from the secondary analysis routine 550. Thesecond level analysis routine 400 is described in detail with referenceto FIGS. 4-1 through 4-5 .

FIG. 4-1 schematically illustrates a first subroutine 415 of the secondlevel analysis routine 400 for on-vehicle camera alignment monitoring,which provides root cause analysis when the faults indicate there areinsufficient features for analysis, i.e., the second, third, ninth andtenth fault codes.

Upon initiation, the first subroutine 415 evaluates the fault code(S401). When none of the second, third, ninth and tenth fault codes isindicated (S401)(N), this iteration ends (S414), and advances to thesecond subroutine 420.

When one of the second, third, ninth and tenth fault codes is indicated(S401)(Y), it is evaluated whether a fault related to the camera 41 hasbeen detected (S402), and if so (Y), the root cause is indicated to be afault with the camera (S403), and this iteration ends (S414).

Otherwise (S402)(N), a pixel intensity distribution for the image 99 iscalculated (S404) and evaluated (Intensity <thrd₁) (S405). When thepixel intensity distribution for the image 99 is less than the firstthreshold thrd₁ (S405)(Y), the root cause is indicated to beinsufficient ambient lighting (S406), and this iteration ends (S414).

FIGS. 5-1 and 5-3 pictorially illustrate raw camera images, with FIG.5-1 showing a first raw camera image that is captured under sunlight,and FIG. 5-3 showing a first raw camera image for the same location anddirection of vehicle operation that is captured at night, i.e., absentambient sunlight. FIG. 5-2 illustrates, in bar graph form, pixelintensity distribution with pixel count shown in relation to intensityfor the raw camera image of FIG. 5-1 , i.e., during daylight. FIG. 5-4illustrates, in bar graph form, pixel intensity distribution with pixelcount shown in relation to intensity for the raw camera image of FIG.5-3 , i.e., at night. The pixel intensity distribution of FIG. 5-2 issufficient for analysis of the on-vehicle camera alignment, but thepixel intensity distribution of FIG. 5-4 is insufficient for analysis ofthe on-vehicle camera alignment.

Otherwise (S405)(Y), features from consecutive ones of the image files99 are buffered (S407).

The quantity of static features of the buffered consecutive ones of theimage files 99 is evaluated, including evaluating portions of thebuffered consecutive ones of the image files 99 for presence or absenceof features (S408). When the quantity of static features of the bufferedconsecutive ones of the image files 99 is greater than a secondthreshold (thrd₂), or if a portion of the buffered consecutive ones ofthe image files 99 has no features (S408)(Y), the root cause isindicated to be a lens blockage, such as dirt, debris, etc. (S413), andthis iteration ends (S414). FIG. 6 pictorially illustrates a raw cameraimage 600 of a ROI in which a portion of the ROI is overshadowed by ablockage 610, which is indicated by a plurality of static features inthe form of points 620.

Otherwise (S408)(N), ambient weather conditions are monitored viaon-vehicle sensors and/or via off-vehicle systems (S409) and evaluated(S410).

When the ambient weather conditions indicate presence of precipitation,snow, fog, smog, or other conditions that may reduce visibility(S410)(Y), the root cause is indicated to be poor weather (S412), andthis iteration ends (S414).

When the ambient weather conditions indicate lack of precipitation,snow, fog, smog, or other conditions that may reduce visibility(S410)(N), the root cause is indicated to be other faults (S411), andthis iteration ends (S414) and advances to the subsequent subroutinesdescribed with reference to FIGS. 4-2 through 4-5 .

FIG. 4-2 describes a second subroutine 420 of the second level analysisroutine 400, which provides root cause analysis when the faults indicatethere are insufficient inlier points in the essential matrix calculationfor triangulation and plane fitting, i.e., the fourth, fifth, eight oreleventh fault codes.

Upon initiation, the second subroutine 420 evaluates the fault code(S421). When none of the fourth, fifth, eight or eleventh fault codes isindicated (S421)(N), this iteration ends (S430), and advances to thethird subroutine 440.

Otherwise (S421)(Y), matched feature pairs for the images are loaded(S422), a 3D motion vector is determined for each of the matched featurepairs (S423), and clustering is performed on the motion vectors toidentify one or more clusters (S424).

The quantity of matched feature pairs in the largest of the clusters isevaluated and compared to a third threshold (S425).

When the quantity of matched feature pairs in the largest of theclusters is less than a third threshold (S425)(Y), a root cause relatedto incorrect feature matching is indicated (S426), and this iterationends (S430).

FIG. 7-1 pictorially illustrates a raw image of a ROI of the camera 41,with a multiplicity of feature pairs 700 shown. The multiplicity offeature pairs includes a first set 711 of the feature pairs 700 that arematched feature pairs, and a second set 712 of the feature pairs 700that are incorrectly matched. FIG. 7-2 schematically illustrates, in 3Dspace, clusters of the multiplicity of feature pairs, including a firstcluster 721 corresponding to the first set 711 of the feature pairs 700of FIG. 7-1 that are matched feature pairs, and a second cluster 722corresponding to the second set 712 of the feature pairs 700 of FIG. 7-1that are incorrectly matched.

Otherwise (S425)(N), when the quantity of matched feature pairs in thesecond largest of the clusters is less than a fourth threshold(S427)(Y), a root cause related to presence of a dynamic object, e.g.,another vehicle, is indicated (S428), and this iteration ends (S430).

FIG. 8-1 pictorially illustrates a raw image of a ROI of the camera 41including a dynamic object, e.g., a passing vehicle 803, being shown. Amultiplicity of feature pairs 800 are also shown. The multiplicity offeature pairs includes a first set 811 of the feature pairs 800 that arematched feature pairs, a second set 812 of the feature pairs 800 thatare incorrectly matched, and a third set 813 of the feature pairs 800indicating the dynamic object. FIG. 8-2 schematically illustrates, in 3Dspace, clusters of the multiplicity of feature pairs, including a firstcluster 821 corresponding to the first set 811 of the feature pairs 800of FIG. 8-1 that are matched feature pairs, a second cluster 822corresponding to the second set 812 of the feature pairs 800 of FIG. 8-1that are incorrectly matched, and a third cluster 823 corresponding tothe third set 813 of the feature pairs 800 that correspond to thedynamic object.

Otherwise (S427)(N), another root cause is indicated (S429), and thisiteration ends (S430).

When this iteration ends (S430), execution of the routine 400 advancesto the subsequent subroutines described with reference to FIGS. 4-3through 4-5 .

FIG. 4-3 describes a third subroutine 440 of the second level analysisroutine 400, which provides root cause analysis when the faults indicatean inaccurate motion vector, i.e., the sixth or seventh fault codes.

Upon initiation, the third subroutine 440 evaluates the fault code(S441). When the sixth nor the seventh fault codes are indicated(S441)(N), this iteration ends (S449), and advances to the fourthsubroutine 460.

Otherwise (S441)(Y), the third subroutine 440 checks for presence of afault with the GPS sensor 49 or the IMU sensor 47 (S442). When there isa fault with the GPS sensor 49 or the IMU sensor 47 (S442)(Y), the rootcause is indicated to be a fault with the GPS sensor 49 or the IMUsensor 47 (S443), and this iteration ends (S449).

Otherwise (S442)(N), a yaw error between the motion vector and apredetermined reference vector is determined (S444), with a mean valueand standard deviation for the yaw error being determined (S445). Whenthe mean yaw error is greater than a fifth threshold, and the standarddeviation of the yaw error is less than a sixth thread (S446)(Y), theroot cause is indicated to be a fault with the mounting of the camera,e.g., with a folding mirror when the camera is on a sideview mirror(S447), and this iteration ends (S449).

Otherwise (S446)(N), another root cause is indicated (S448) and thisiteration ends (S449).

When this iteration ends (S449), execution of the routine 400 advancesto the subsequent subroutines described with reference to FIGS. 4-4through 4-5 .

FIG. 4-4 describes a fourth subroutine 460 of the second level analysisroutine 400, which provides root cause analysis related to the motionvector.

Initially, each raw image is subjected to an undistortion process, whichinclude converting the raw image captured using a fish-eye lens to a 2Dimage or 3D image (S461). After being undistorted, the image issubjected to feature matching and detection processes (S462), vanishingpoint detection (S463), and a vanishing point quality check (S464). Thevanishing point is evaluated to determine if it is stable (S465), and ifnot (S465)(N), the previous steps are repeated.

When the vanishing point is stable (S465)(Y), an epipolar line normalvector is determined based upon the stable vanishing point (S466).

When a change in the vector angle for the epipolar line normal vector isless than a threshold (S467)(Y), it indicates that the mounting of thecamera 41 is intact (S469). When a change in the vector angle for theepipolar line normal vector is greater than the threshold (S467)(N), itindicates that the mounting of the camera 41 has been compromised, e.g.,by folding (S468). This result is reported out, and the execution of theroutine 400 advances to the subsequent subroutine described withreference to FIG. 4-5 .

FIG. 4-5 describes the fifth subroutine 480 of the second level analysisroutine 400, which provides root cause analysis related to alignment ofthe camera 41.

The alignment of the camera is evaluated (S481). If a misalignment hasnot been detected (S481)(N), this iteration ends (S485). If amisalignment has been detected (S481)(Y), this iteration evaluateswhether the twelfth fault code has been generated (S482). When thetwelfth fault code has been generated (S482)(Y), a root cause of curbdetection is indicated (S483) and this iteration ends (S485). When thetwelfth fault code has not been generated (S482)(N), another root causeis indicated (S484) and this iteration ends (S485).

Referring again to FIG. 2 , the post-processing analysis routine 500 iscomposed of a plurality of actions to aggregate and save data fromconsecutive image files, provide notification of a fault, control,adapt, disable or otherwise mitigate vehicle operation in view of afault, and adapt operation based upon occurrence of a cameraalignment-related fault.

The action to aggregate and save data from consecutive image files mayinclude evaluating diagnostic results (i.e., from the first levelanalysis routine 300 and the second level analysis routine 400) fromconsecutive images within a time window to identify a root cause. Whenthe same root cause is detected for over a quantity of n files withinthe time window, the result is output. Otherwise, the result may besubject to statistical analysis to identify a trend over a longer timeperiod. This action also includes capturing and storing the images 99along with metadata related to the analysis and outputs from the firstlevel analysis routine 300 and the second level analysis routine 400 fora period of time. This may include communicating all or a portion of thecamera images 99 and metadata to an off-board system.

The action to provide notification of the camera alignment-related faultmay include generating and sending a visual, auditory, haptic, or othermessage notifying the vehicle operator of the camera alignment-relatedfault. This action to provide notification may also include generating aservice request to a vehicle service center.

The action to control, adapt, disable or otherwise mitigate vehicleoperation in view of occurrence of the camera alignment-related faultincludes disabling systems having functions that rely upon the accurateoperation of the camera 41, e.g., disabling the autonomic vehiclecontrol system 20 that relies upon the camera 41. In this manner, thevarious features can indicate faults of the camera alignment systememploying logic to isolate faults and explicitly classify different rootcauses of faults.

In addition, the system, method, and/or apparatus 200 to monitoralignment may automatically adjust mechanical alignment of therespective one of the cameras 41 in relation to the vehicle, or mayadjust internal parameters of the respective one of the cameras 41, suchas lens angle, focal length, filtering, etc.

On-vehicle cameras are subjected to dynamically changing internal andexternal factors that may affect alignment, and thus affect operation ofthe on-vehicle systems whose operations rely upon the camera images. Theconcepts described herein provide a method, system and/or apparatus thatis able to capture an image file from the on-vehicle camera; execute afirst level analysis of the image file, the vehicle operatingparameters, and the camera signal parameters to detect dynamicconditions and a plurality of image feature parameters that affectcamera alignment; detect an error with one of the dynamic conditions orthe plurality of image feature parameters that affects the cameraalignment; execute a second level analysis of the camera signalparameters to identify a root cause indicating one of the dynamicconditions or the image feature parameters that affects the cameraalignment based upon the error; detect a camera alignment-related faultbased upon the root cause; and control vehicle operation based upon thecamera alignment-related fault. Accordingly, the claimed embodimentseffectuate an improvement in the technical field.

The flowchart and block diagrams in the flow diagrams illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which includes one or more executable instructions forimplementing the specified logical function(s). It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustrations, may be implemented by special-purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial-purpose hardware and computer instructions. These computerprogram instructions may also be stored in a computer-readable mediumthat can direct a controller or other programmable data processingapparatus to function in a particular manner, such that the instructionsstored in the computer-readable medium produce an article of manufactureincluding instructions to implement the function/act specified in theflowchart and/or block diagram block or blocks.

As used herein, the term “system” may refer to one of or a combinationof mechanical and electrical actuators, sensors, controllers,application-specific integrated circuits (ASIC), combinatorial logiccircuits, software, firmware, and/or other components that are arrangedto provide the described functionality.

The use of ordinals such as first, second and third does not necessarilyimply a ranked sense of order, but rather may distinguish betweenmultiple instances of an act or structure.

The detailed description and the drawings or figures are supportive anddescriptive of the present teachings, but the scope of the presentteachings is defined solely by the claims. While some of the best modesand other embodiments for carrying out the present teachings have beendescribed in detail, various alternative designs and embodiments existfor practicing the present teachings defined in the claims.

What is claimed is:
 1. A system for on-vehicle camera alignmentmonitoring, comprising: a vehicle spatial monitoring system including anon-vehicle camera in communication with a controller, the controllerincluding an instruction set that is executable to: monitor vehicleoperating parameters and camera signal parameters; capture an image filefrom the on-vehicle camera; execute a first level analysis of the imagefile, the vehicle operating parameters, and the camera signal parametersto detect dynamic conditions and a plurality of image feature parametersthat affect camera alignment; detect an error with one of the dynamicconditions or the plurality of image feature parameters that affects thecamera alignment; execute a second level analysis of the camera signalparameters to identify a root cause indicating one of the dynamicconditions or the image feature parameters that affects the cameraalignment based upon the error; detect a camera alignment-related faultbased upon the root cause; and control vehicle operation based upon thecamera alignment-related fault.
 2. The system of claim 1, wherein thedynamic conditions include vehicle speed, acceleration and yaw rate, andwherein the instruction set is executable to detect the error when oneof the vehicle speed, the acceleration, or the yaw rate is outside arespective allowable range based upon the dynamic conditions.
 3. Thesystem of claim 1, wherein the dynamic conditions include a roadsurface, and wherein the instruction set is executable to detect theerror when an uneven road surface is detected.
 4. The system of claim 1,wherein the plurality of image feature parameters includes at least oneof a feature extraction count, a feature match count, an essentialmatrix inlier point count, a recovering pose feature count, atriangulation inlier point count, a two-dimensional road region ofinterest (ROI) feature point count, a three-dimensional road ROI featurepoint count, and a plane-fitting inlier point count.
 5. The system ofclaim 4, wherein the instruction set is executable to detect the errorwhen the one of the plurality of image feature parameters is outside arespective allowable range.
 6. The system of claim 5, wherein theinstruction set being executable to execute the second level analysis ofthe camera signal parameters to identify a root cause associated withthe plurality of image feature parameters comprises the instruction setbeing executable to identify one of an insufficient lighting condition,a lens blockage, or an inclement weather condition when one of theplurality of image feature parameters is outside a respective allowablerange.
 7. The system of claim 1, further comprising the instruction setbeing executable to capture a plurality of consecutive image files fromthe on-vehicle camera; wherein the instruction set is executable to:determine a plurality of matched feature pairs between the plurality ofconsecutive image files; determine a plurality of motion vectors basedupon the plurality of matched feature pairs; detect an error with theplurality of motion vectors that affects the camera alignment; andexecute the second level analysis of the camera signal parameters toidentify the root cause of the error with the plurality of motionvectors that affects the camera alignment; wherein the root cause of theerror with the plurality of motion vectors that affects the cameraalignment includes a fault with mounting of the on-vehicle camera. 8.The system of claim 7, further comprising the instruction set beingexecutable to detect a camera alignment-related fault based upon theroot cause of the error with the plurality of motion vectors thataffects the camera alignment; and control vehicle operation based uponthe camera alignment-related fault.
 9. The system of claim 7, furthercomprising the instruction set being executable to cluster the pluralityof motion vectors to determine inlier points in an essential matrixcalculation, a triangulation and plane fitting; and identify aninsufficient quantity of inlier points based thereon.
 10. The system ofclaim 1, wherein the instruction set being executable to control vehicleoperation based upon the camera alignment-related fault comprises theinstruction set being executable to notify a vehicle operator of thecamera alignment-related fault.
 11. The system of claim 1, furthercomprising an autonomic vehicle control system operatively connected tothe vehicle spatial monitoring system; wherein the instruction set beingexecutable to control operation based upon the camera alignment-relatedfault comprises the instruction set being executable to disable theautonomic vehicle control system based upon the camera alignment-relatedfault.
 12. The system of claim 1, further comprising communicating theimage file, the vehicle operating parameters, the camera signalparameters, and the plurality of image feature parameters to anoff-board system.
 13. A system for on-vehicle camera alignmentmonitoring, comprising: a vehicle spatial monitoring system including anon-vehicle camera in communication with a controller, the controllerincluding an instruction set that is executable to: monitor vehicleoperating parameters and camera signal parameters; capture a pluralityof image files from the on-vehicle camera; analyze the plurality ofimage files, the vehicle operating parameters, and the camera signalparameters to detect dynamic conditions, a plurality of image featureparameters, and a plurality of motion vectors that affect cameraalignment; detect an error with one of the dynamic conditions, theplurality of image feature parameters, and the plurality of motionvectors that affects the camera alignment; analyze the camera signalparameters to identify a root cause indicating one of the dynamicconditions, the image feature parameters, or the plurality of motionvectors that affects the camera alignment based upon the error; detect acamera alignment-related fault based upon the root cause; and controlvehicle operation based upon the camera alignment-related fault.
 14. Thesystem of claim 13, wherein the plurality of image feature parametersincludes at least one of a feature extraction count, a feature matchcount, an essential matrix inlier point count, a recovering pose featurecount, a triangulation inlier point count, a two-dimensional road ROIfeature point count, a three-dimensional road ROI feature point count,and a plane-fitting inlier point count.
 15. The system of claim 14,wherein the instruction set is executable to detect the error when theone of the plurality of image feature parameters is outside a respectiveallowable range.
 16. The system of claim 15, wherein the instruction setbeing executable to identify a root cause associated with the pluralityof image feature parameters comprises the instruction set beingexecutable to identify one of an insufficient lighting condition, a lensblockage, or an inclement weather condition when one of the plurality ofimage feature parameters is outside a respective allowable range. 17.The system of claim 13, further comprising the instruction set beingexecutable to capture a plurality of consecutive image files from theon-vehicle camera; wherein the instruction set is executable to:determine a plurality of matched feature pairs between the plurality ofconsecutive image files; determine a plurality of motion vectors basedupon the plurality of matched feature pairs; detect an error with theplurality of motion vectors that affects the camera alignment; andidentify the root cause of the error with the plurality of motionvectors that affects the camera alignment; wherein the root cause of theerror with the plurality of motion vectors that affects the cameraalignment includes a fault with mounting of the on-vehicle camera. 18.The system of claim 17, further comprising the instruction set beingexecutable to: detect a camera alignment-related fault based upon theroot cause of the error with the plurality of motion vectors thataffects the camera alignment; and control vehicle operation based uponthe camera alignment-related fault.
 19. The system of claim 17, furthercomprising the instruction set being executable to cluster the pluralityof motion vectors to determine inlier points in an essential matrixcalculation, a triangulation and plane fitting; and identify aninsufficient quantity of inlier points based thereon.
 20. A system foron-vehicle camera alignment monitoring, comprising: a vehicle spatialmonitoring system including an on-vehicle camera in communication with acontroller, and an autonomic vehicle control system operativelyconnected to the vehicle spatial monitoring system, the controllerincluding an instruction set that is executable to: monitor vehicleoperating parameters and camera signal parameters; capture a pluralityof image files from the on-vehicle camera; analyze the plurality ofimage files, the vehicle operating parameters, and the camera signalparameters to detect dynamic conditions, a plurality of image featureparameters, and a plurality of motion vectors that affect cameraalignment; detect an error with one of the dynamic conditions, theplurality of image feature parameters, and the plurality of motionvectors that affects the camera alignment; analyze the camera signalparameters to identify a root cause indicating one of the dynamicconditions, the image feature parameters, or the plurality of motionvectors that affects the camera alignment based upon the error; detect acamera alignment-related fault based upon the root cause; and disablethe autonomic vehicle control system based upon the cameraalignment-related fault.